How to Achieve Personalization with Learning and Optimization

Nearly all marketers today can agree that personalized marketing trumps a one-size-fits-all model. However, the biggest challenge is actually achieving truly 1:1 personalization, as it happens to be very, very difficult. Every customer behaves differently, with their behaviors motivated by different factors, and on top of that, these behaviors and motivators are continually changing. At SessionM, we advocate for a process called learning and optimization to tackle the challenge of personalizing marketing campaigns.

Personalization cannot be achieved and perfected overnight, but instead requires the persistent application off an intentional learning approach combined with lots of practice. The premise behind learning and optimization is that, as a marketer, every touchpoint you have with a customer should be utilized as a learning opportunity to help you better understand, engage, and ultimately maximize the value of that relationship.

Too often, we come across marketing organizations that plan out fixed marketing calendars as much as a year in advance. While this type of preparation is admirable, it generally limits the rate of learning and is built on unclear learning objectives. Instead, it would be better to be planful while also leaving enough flexibility to draw learnings from one campaign to apply to the next.

So what does the learning and optimization process involve?

Define your objectives and hypotheses

Begin by defining long-term objectives and identifying the potential key drivers to reach those goals. Before we launch into campaigns, it is critical that we design with clear hypotheses in mind in order to maximize our learnings. What do we expect to find as outcomes of the test, and how will different potential outcomes inform our next moves?

Plan out multiple iterations of campaigns

The key to learning and optimization is to avoid designing campaigns as standalone experiments, but rather to design two steps ahead and always build off derived insights. This enables each iteration of testing to efficiently close in on the end goal the moment campaign results are analyzed. Within a few rounds of testing, you will be ready to reap the rewards of your valuable insights.

Focus on generating learnings first and impact later.

By investing early in learnings, we can maximize future impact by translating those learnings into optimization of goals (e.g., incremental revenue) over time. At first, the results might not look so great. Some campaigns early on might lose out with negative incremental revenue, but with multiple iterations that build upon one another, performance will inevitably begin to trend upward.

Achieving personalization requires not only time, but also a consistent, deliberate system for learning and optimization. This approach, coupled with a great deal of practice, empowers marketers to utilize every customer touchpoint as a chance to better understand your customers and identify the most impactful way to engage them in order to motivate the right behaviors and increase loyalty.